Market Basket Analysis Linear Discriminant Analysis with R

Market Basket Analysis & Linear Discriminant Analysis with R, available at $39.99, has an average rating of 4.35, with 36 lectures, 2 quizzes, based on 66 reviews, and has 449 subscribers.

You will learn about Students will know what is association rules (Market Basket Analysis)? How do association rules work? How to do market basket analysis using Excel & R What is linear discriminant analysis? How to do linear discriminant analysis using R? How to understand each component of the linear discriminant analysis output? Practical usage of linear discriminant analysis This course is ideal for individuals who are Market Research Professionals or Business Analytics professionals or Data Scientists It is particularly useful for Market Research Professionals or Business Analytics professionals or Data Scientists.

Enroll now: Market Basket Analysis & Linear Discriminant Analysis with R

Summary

Title: Market Basket Analysis & Linear Discriminant Analysis with R

Price: $39.99

Average Rating: 4.35

Number of Lectures: 36

Number of Quizzes: 2

Number of Published Lectures: 36

Number of Published Quizzes: 2

Number of Curriculum Items: 39

Number of Published Curriculum Objects: 39

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Students will know what is association rules (Market Basket Analysis)?

How do association rules work?

How to do market basket analysis using Excel & R

What is linear discriminant analysis?

How to do linear discriminant analysis using R?

How to understand each component of the linear discriminant analysis output?

Practical usage of linear discriminant analysis

Who Should Attend

Market Research Professionals

Business Analytics professionals

Data Scientists

Target Audiences

Market Research Professionals

Business Analytics professionals

Data Scientists

This course has two parts. In part 1 Association rules (Market Basket Analysis) is explained. In Part 2, Linear Discriminant Analysis (LDA) is explained. L

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Details of Part 1 – Association Rules / Market Basket Analysis (MBA)

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What is Market Basket Analysis (MBA) or Association rules

Usage of Association Rules – How it can be applied in a variety of situations?

How does an association rule look like?

Strength of an association rule –?

1. Support measure
2. Confidence measure?
3. Lift measure

Basic Algorithm to derive rules

Demo of Basic Algorithm to derive rules – discussion on breadth first algorithm and depth first algorithm

Demo Using R – two examples

Assignment to fortify concepts

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Details of Part 2 – Linear? (Market Basket Analysis)

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Need of a classification model

Purpose of Linear Discriminant

A use case for classification

Formal definition of LDA

Analytics techniques applicability

Two usage of LDA?

1. LDA for Variable Selection
2. Demo of using LDA for Variable Selection
3. Second usage of LDA – LDA for classification

Details on second practical usage of LDA

1. Understand which are three important component to understand LDA properly
2. First complexity of LDA – measure distance :Euclidean distance?
3. First complexity of LDA – measure distance enhanced? :Mahalanobis distance
4. Second complexity of LDA – Linear Discriminant function
5. Third complexity of LDA – posterior probability / Bays theorem

Demo of LDA using R

1. Along with jack knife approach
2. Deep dive into LDA outputn
3. Visualization of LDA operations
4. Understand the LDA chart statistics

LDA vs PCA side by side

Demo of LDA for more than two classes: understand

1. Data visualization
2. Model development
3. Model validation on train data set and test data sets

Industry usage of classification algorithm

Handling Special Cases in LDA

Course Curriculum

Chapter 1: Part 1 – Association Rules (Market Basket Analysis)

Lecture 1: Section Overview

Lecture 2: How to study this course?

Lecture 3: What is Market Basket Analysis (MBA) / Association rules ?

Lecture 4: Usage of Association Rules

Lecture 5: How does an association rule look like?

Lecture 6: Strength of an association rule – Support measure

Lecture 7: Strength of an association rule – Confidence measure

Lecture 8: Strength of an association rule – Lift measure

Lecture 9: Basic Algorithm to derive rules

Chapter 2: Part 1- Association rules demo & quiz

Lecture 1: Demo of Basic Algorithm to derive rules (BFS and DFS)

Lecture 2: Demo Using R on Fruit transaction data

Lecture 3: Demo Using R on another transaction data

Lecture 4: Try your learning – assignment

Lecture 5: Assignment solution

Chapter 3: Part 2 – Linear Discriminant Analysis (LDA)

Lecture 1: Section Overview

Lecture 2: Need of a classification model

Lecture 3: Purpose of Linear Discriminants

Lecture 4: A case for classification

Lecture 5: Formal definition of LDA

Lecture 6: Analytics techniques applicability

Lecture 7: First practical use of LDA – LDA for Variable Selection

Lecture 8: Demo of using LDA for Variable Selection

Chapter 4: Part 2 : Second practical usage of LDA – LDA for classification

Lecture 1: Intuitive Understanding of LDA for classification

Lecture 2: First complexity : distance calculation – Euclidean distance

Lecture 3: First complexity : distance calculation (enhanced) – Mahalanobis distance 01

Lecture 4: First complexity : distance calculation (enhanced) – Mahalanobis distance 02

Lecture 5: Second complexity : Linear Discriminant Function

Lecture 6: Third complexity : Posterior Probability (Bays Theorem)

Lecture 7: Demo of LDA using R part 01

Lecture 8: Demo of LDA using R part 02

Lecture 9: LDA vs PCA side by side

Lecture 10: Demo of LDA for more than two classes – part 01

Lecture 11: Demo of LDA for more than two classes – part 02

Lecture 12: Industrial usage of LDA

Lecture 13: Handling Special Cases (biased sample / differential misclassification) in LDA

Lecture 14: Closing Note

Instructors

Gopal Prasad Malakar
Trains Industry Practices on data science / machine learning

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 8 votes

4 stars: 33 votes

5 stars: 22 votes

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